ABSTRACT
To this point we have seen the basic elements of Bayesian methods, arguments on behalf of their use from several different philosophical standpoints, and an assortment of computational algorithms for carrying out the analysis. We have observed that the generality of the methodology coupled with the power of modern computing enables consideration of a wide variety of hierarchical models for a given dataset. Given all this, the most natural questions for the reader to ask might be:
1. How can I tell if any of the assumptions I have made (e.g., the specific choice of prior distribution) is having an undue impact on my results?
